Image classification techniques implemented today provide respectable precision and recall for image recognition processing. However, precision and recall can stand to be improved. Consider an example where an image of a building is taken from a specific angle. An image retrieval database may contain images of that building from a variety of angles but may not include an image from the specific angle as taken by a user. When the image retrieval database is searched, nearest neighbor images may be identified to find the closest predicted match for image recognition purposes. This leaves doubt as to the correctness of the image recognition prediction. As such, processing that increases precision and recall for image recognition provides great technical benefit for computing devices (interfacing with applications/services) for image recognition processing.
Furthermore, most of the image classification techniques today are limited to scale through large amount training data for, example, where modeling may be trained based on an index of various images or a set of categories within a segment. While this may provide high precision for image classification it is very challenging to scale for problem space where users of applications/services expect systems to be able to classify any object that user points camera to with both high recall and high precision. As more and more image content and/or classification data becomes available, data models need to be adapted to be able to account for such data (and further be trained based on such data).
Presently, most image classification techniques are related to individual data models that suffer from precision shortcomings, recall shortcomings and/or scalability issues. There is usually a trade-off where an implemented solution may be able to obtain high precision (or high recall) in image recognition but suffer from low recall (or low precision). For instance, a segment agnostic index (e.g. text/terms/entities) may be used to classify image content. While such a solution produces high recall for image recognition, it suffers from low precision in correctness of image recognition processing. In other instances, image-based classifications models may be implemented. Image-based classification models may produce high-precision for image recognition but such an approach may lack scalability when new segments are added. For instance, a large amount of training is required to integrate a new segment into an image-based classification model. In other instances, a retrieval database may be configured for image recognition processing (e.g. identify a nearest neighbor image). While such a solution produces adequate recall and precision, it also suffers from scalability issues when classification categories are introduced (and may rapidly change).